CN117637114A - Clinical nutrition diagnosis and treatment simulation system - Google Patents
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
- G16H20/60—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to nutrition control, e.g. diets
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
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Abstract
The invention discloses a clinical nutrition diagnosis and treatment simulation system, which particularly relates to the technical field of nutrition intervention, and the clinical nutrition diagnosis and treatment simulation system comprises a patient information module, a nutrition evaluation module, a neural network training module, a diagnosis and treatment scheme making module, a doctor reporting module and an intelligent reminding module, wherein a scientific secondary nutrition diagnosis and treatment scheme is firstly generated, satisfaction of a patient and executable time of the patient are obtained, a determination coefficient is generated, the determination coefficient is compared with a preset determination coefficient threshold value, if the determination coefficient is smaller than the preset determination coefficient threshold value, the doctor modifies the secondary nutrition diagnosis and treatment scheme on the basis of the secondary nutrition diagnosis and treatment scheme, and the satisfaction of the patient and the executable time of the patient are collected again until the determination coefficient is larger than the preset determination coefficient threshold value, and the doctor dynamically adjusts the secondary nutrition diagnosis and treatment scheme on the basis of retaining the secondary nutrition diagnosis and treatment scheme so as to ensure that the finally generated treatment scheme is more fit with actual conditions and requirements of the patient and is sent to the patient through the intelligent reminding module.
Description
Technical Field
The invention relates to the technical field of nutrition intervention, in particular to a clinical nutrition diagnosis and treatment simulation system.
Background
Currently, malnutrition is always one of the important factors affecting human health, directly leading to "three-up three-down" poor clinical outcome: increasing hospital stays, complications rates, and mortality from illness; reducing the diagnosis and treatment effect, the quality of life and the survival time of the diseases. Meanwhile, rapid progress is also made in aspects such as infusion technology, nutrition preparation, disease metabolism research and the like. The nutrition diagnosis and treatment not only plays an active role in improving the nutrition condition of inpatients, reducing the risk of malnutrition occurrence and the like, but also has a certain influence on improving the comprehensive diagnosis and treatment level of hospitals, improving the clinical curative effect, reducing the medical cost and the like, and a clinical nutrition diagnosis and treatment simulation system is arranged for solving the problem, so that the malnutrition problem of the patients is better regulated.
The prior art has the following defects:
most of the current clinical nutrition diagnosis and treatment simulation systems are used for directly generating and transmitting information of a patient to generate a relatively fixed treatment scheme, and lack of deep consideration on subjective feeling of the patient; in addition, if the acceptance of the generated plan patient is low, the patient cannot be flexibly changed, and the diagnosis and treatment effect of the patient may be affected.
In order to solve the above-mentioned defect, a technical scheme is provided.
Disclosure of Invention
In order to overcome the above-mentioned drawbacks of the prior art, embodiments of the present invention provide a clinical nutrition diagnosis and treatment simulation system to solve the above-mentioned problems.
In order to achieve the above purpose, the present invention provides the following technical solutions:
a clinical nutrition diagnosis and treatment simulation system comprises a patient information module, a nutrition evaluation module, a neural network training module, a diagnosis and treatment scheme making module, a doctor reporting module and an intelligent reminding module, wherein the modules are connected through signals;
patient information module: the model training module is used for acquiring and storing basic information of a patient and transmitting the data to the model training module;
nutrition evaluation module: for collecting and analyzing physiological, nutritional and clinical data of a patient and communicating the data to a model training module;
the neural network training module: training a patient by using a neural network based on the data uploaded by the patient information module and the nutrition evaluation module to obtain a preliminary personalized nutrition diagnosis and treatment scheme, and transmitting the obtained data to a diagnosis and treatment scheme making module;
diagnosis and treatment scheme making module: according to the data uploaded by the neural network training module, the obtained primary personalized nutrition diagnosis and treatment scheme is finely modified by combining with the information of the patient, a secondary nutrition diagnosis and treatment scheme is obtained, and the modified secondary nutrition diagnosis and treatment scheme is transmitted to the doctor reporting module;
doctor reporting module: the modified diagnosis and treatment scheme uploaded by the diagnosis and treatment scheme making module is transmitted to a doctor, the doctor modifies or does not modify according to the specific physical condition of the patient, and the final result is transmitted to the intelligent reminding module;
and the intelligent reminding module is used for: and according to the final diagnosis and treatment scheme information uploaded by the doctor reporting module, the intelligent reminding module sends personalized reminding and guiding information to the patient.
In a preferred embodiment, the patient information module includes data of age, sex, prior medical history, body fat rate of the patient.
In a preferred embodiment, the nutritional assessment module comprises average daily nutritional intake of the patient, patient metabolic compliance rate;
the acquisition logic of the metabolic standard reaching rate of the patient is as follows: sh= (Sn/Sf-Sg), wherein Sh is the patient metabolism standard rate and Sg is the standard metabolism standard rate; sn is the average daily energy metabolized by the patient and Sf is the average daily energy ingested by the patient.
In a preferred embodiment, the doctor reporting module comprises the steps of:
s1: the doctor receives the secondary nutrition diagnosis and treatment scheme uploaded by the diagnosis and treatment scheme making module, and communicates the secondary nutrition diagnosis and treatment scheme with the patient to obtain the satisfaction degree of the patient and the executable time of the patient;
s2: marking satisfaction degree of a patient and executable duration of the patient as Dq and Eq respectively, establishing a determination coefficient W according to the satisfaction degree Dq of the patient and the executable duration Eq of the patient, and comparing the determination coefficient W with a preset determination coefficient threshold WA;
s3: if the determined coefficient W is larger than a preset determined coefficient threshold WA, determining the secondary nutrition diagnosis and treatment scheme as a final diagnosis and treatment scheme of the patient, and sending the final diagnosis and treatment scheme to the patient through an intelligent reminding module;
s4: if the determined coefficient W is smaller than the preset determined coefficient threshold WA, modifying the secondary nutrition diagnosis and treatment scheme by a doctor, and collecting the satisfaction Dq of the patient and the executable duration Eq of the patient again until the determined coefficient W is larger than the preset determined coefficient threshold WA.
In a preferred embodiment, the acquiring logic of the determining coefficient W is:
comprehensively processing satisfaction Dq of the patient and executable duration Eq of the patient, establishing a data processing model, and generating a determination coefficient W according to the following formula:
wherein, alpha and gamma are respectively preset proportionality coefficients of satisfaction Dq of the patient and executable duration Eq of the patient, and the alpha and the gamma are both larger than 0.
The invention has the technical effects and advantages that:
1. the invention generates a scientific secondary nutrition diagnosis and treatment scheme by setting a patient information module, a nutrition evaluation module, a neural network training module, a diagnosis and treatment scheme making module, a doctor reporting module and an intelligent reminding module, obtains the satisfaction degree of a patient and the executable time length of the patient, generates a determination coefficient, compares the determination coefficient with a preset determination coefficient threshold value, determines the secondary nutrition diagnosis and treatment scheme as a final diagnosis and treatment scheme of the patient if the determination coefficient is larger than the preset determination coefficient threshold value, and sends the determination coefficient to the patient through the intelligent reminding module;
2. if the determined coefficient is smaller than the preset determined coefficient threshold, the doctor modifies the secondary nutrition diagnosis and treatment scheme and re-collects the satisfaction degree of the patient and the executable time of the patient until the determined coefficient is larger than the preset determined coefficient threshold, and the doctor dynamically adjusts the secondary nutrition diagnosis and treatment scheme on the basis of retaining the secondary nutrition diagnosis and treatment scheme so as to ensure that the finally generated treatment scheme is more fit with the actual condition and the requirement of the patient.
Drawings
For the convenience of those skilled in the art, the present invention will be further described with reference to the accompanying drawings;
FIG. 1 is a schematic block diagram of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
FIG. 1 shows a clinical nutrition diagnosis and treatment simulation system of the invention, which comprises a patient information module, a nutrition evaluation module, a neural network training module, a diagnosis and treatment scheme making module, a doctor reporting module and an intelligent reminding module, wherein the modules are connected through signals;
patient information module: the model training module is used for acquiring and storing basic information of a patient and transmitting the data to the model training module;
nutrition evaluation module: for collecting and analyzing physiological, nutritional and clinical data of a patient and communicating the data to a model training module;
the neural network training module: training a patient by using a neural network based on the data uploaded by the patient information module and the nutrition evaluation module to obtain a preliminary personalized nutrition diagnosis and treatment scheme, and transmitting the obtained data to a diagnosis and treatment scheme making module;
diagnosis and treatment scheme making module: according to the data uploaded by the neural network training module, the obtained primary personalized nutrition diagnosis and treatment scheme is finely modified by combining with the information of the patient, a secondary nutrition diagnosis and treatment scheme is obtained, and the modified secondary nutrition diagnosis and treatment scheme is transmitted to the doctor reporting module;
doctor reporting module: the modified secondary nutrition diagnosis and treatment scheme uploaded by the diagnosis and treatment scheme making module is transmitted to a doctor, the doctor modifies or does not modify according to the specific condition of the patient, and the final result is transmitted to the intelligent reminding module;
and the intelligent reminding module is used for: and according to the final diagnosis and treatment scheme information uploaded by the doctor reporting module, the intelligent reminding module sends personalized reminding and guiding information to the patient.
The specific operation is as follows:
basic information of patient: refers to the age, sex, past history and body fat rate of the patient; these data are relatively easy to collect and important; these basic data provide a visual and comprehensive representation of the patient's health to the medical professional as compared to other more complex physical information;
collecting the age and sex of the patient for initial screening according to physiological differences of the patient; age and gender are basic identifiers of individual physiological characteristics, and are critical to understanding physiological differences and metabolic changes of patients; for example, women may experience an inoculation process at the birth age, requiring more attention to the intake of elemental iron, while the elderly may be faced with problems with decreased bone density, requiring more attention to the intake of calcium and vitamin D. For example: a 45 year old female patient may need to pay special attention to the intake of elemental iron because she may be at childbearing age and blood will be lost during menstruation, requiring more iron to compensate for the loss in the body.
The past medical history of the patient is collected to help evaluate the chronic disease risk of the patient, the influence of medicine diagnosis and treatment on nutrition and the operation history. For example, patients with diabetes may require tighter glycemic control, while patients with hypertension may require limited intake of sodium. For example, a patient suffering from diabetes may need to control carbohydrate intake, moderately increase dietary fiber, to maintain blood glucose stability.
The body fat rate of a patient is collected for analysis of the body composition of the patient, and is an important indicator for assessing obesity and lean body mass of the patient, and is critical for determining proper weight management and nutritional intervention, and different body fat rates may require different dietary and exercise interventions. For example, a patient may have a high body fat rate and may need to be dieted for low calorie, high fiber diet in combination with moderate aerobic exercise to reduce body fat rate and improve body composition.
By comprehensively analyzing these basic information, medical professionals can more fully understand the patient's physical condition, health risks, and potential nutritional issues. The method provides scientific basis for making personalized nutrition diagnosis and treatment scheme. In practice, specialized tools and techniques may be used for measurement and analysis, such as measuring body fat rate using a bioelectrical impedance meter, integrating the patient's past medical history through an electronic medical record system.
Physiological, nutritional and clinical data of patients: refers to the average daily nutrient intake of the patient and the up-to-standard rate of the patient's metabolism;
average daily nutrient intake by patients: refers to average daily energy intake, protein intake, fat and carbohydrate intake, vitamin and mineral intake of a patient, and the average daily energy, protein, fat and carbohydrate, vitamin and mineral intake of the patient are collected to judge whether the daily nutritional intake of the patient is standard or not, and a specific diagnosis and treatment scheme is formulated according to the daily nutritional intake of the patient; these data can be obtained through mobile phone applications and online platforms, which can be used by patients to easily enter daily food intake, some applications also providing an estimate of nutritional composition.
Patient metabolism achievement rate: the metabolic efficiency of the body of the patient for intake of the nutrient substances is critical to ensure the normal operation of the body according to the normal level of the metabolic rate of the patient, the daily nutrient intake amount calculates the metabolic standard reaching rate of the patient, and if the metabolic standard reaching rate of the patient is smaller, the metabolic efficiency of the body for intake of the nutrient substances is possibly reflected to be relatively lower.
The acquisition logic of the metabolic standard rate of the patient is as follows: sh= (Sn/Sf-Sg), wherein Sh is the patient metabolism standard rate and Sg is the standard metabolism standard rate; sn is the average daily energy metabolized by the patient, sf is the average daily energy ingested by the patient;
the standard metabolism standard rate is calculated according to the average daily energy intake of the patient and the basic information of the patient, and the standard metabolism standard rate of the patient is obtained through intelligent calculation of the system; the average daily energy of the patient can be collected by some metabolic instruments; and according to the metabolism standard reaching rate of the patient, a nutrition diagnosis and treatment scheme of the patient is formulated more accurately.
The neural network training module: training the patient by using a neural network for the data uploaded by the patient information module and the nutrition evaluation module, so as to obtain a preliminary personalized nutrition diagnosis and treatment scheme, and obtaining a scientific diagnosis and treatment scheme according to basic information of the patient and basic scheme obtained by physiological, nutrition and clinical data of the patient;
the diagnosis and treatment scheme making module makes a slight modification to the resulting preliminary personalized nutrition diagnosis and treatment scheme, and the scheme primarily generated by the neural network is assumed to suggest that the patient ingests 2000 kcal of energy per day, including proper proteins, fats and carbohydrates. The diagnosis and treatment scheme making module can be finely adjusted according to special conditions of patients, and can provide more specific food selection suggestions for the patients according to diet preference, taste habit and possible food tabu of the patients, or replace some foods of the step-down personalized nutrition diagnosis and treatment scheme, for example, for patients loving a good pasta, the preliminary personalized nutrition diagnosis and treatment scheme is assumed to suggest that the patients ingest cooked rice every day, and the cooked rice in the scheme can be replaced by pasta with the same heat; for patients who prefer vegetarian foods, the initially generated regimen of the neural network may include animal-derived proteins, and the regimen making module may recommend vegetable protein-rich foods, such as legumes, nuts, soy products, and the like, to ensure that the patient obtains sufficient protein. For example, animal proteins are replaced with tofu or legume products. For patients suffering from food allergy or special food contraindications, the diagnosis and treatment scheme making module can adjust the scheme according to the special conditions of the patients. For example, if the preliminary regimen includes foods that are allergic to the patient, these foods may be replaced with substitutes of similar calories and nutrients to ensure that the patient's diet does not elicit an allergic reaction.
It is to be reminded that the diagnosis and treatment scheme making module collects and stores the dietary habits of the patient, such as whether the patient prefers vegetarian diet, allergens of the patient, dietary customs of the patient, and the like.
The doctor reporting module comprises the following steps:
s1: the doctor receives the secondary nutrition diagnosis and treatment scheme uploaded by the diagnosis and treatment scheme making module, and communicates the secondary nutrition diagnosis and treatment scheme with the patient to obtain the satisfaction degree of the patient and the executable time of the patient;
s2: marking satisfaction degree of a patient and executable duration of the patient as Dq and Eq respectively, establishing a determination coefficient W according to the satisfaction degree Dq of the patient and the executable duration Eq of the patient, and comparing the determination coefficient W with a preset determination coefficient threshold WA;
s3: if the determined coefficient W is larger than a preset determined coefficient threshold WA, determining the secondary nutrition diagnosis and treatment scheme as a final diagnosis and treatment scheme of the patient, and sending the final diagnosis and treatment scheme to the patient through an intelligent reminding module;
s4: if the determined coefficient W is smaller than a preset determined coefficient threshold WA, modifying the secondary nutrition diagnosis and treatment scheme by a doctor, and collecting the satisfaction degree of the patient and the executable time length of the patient again, wherein the determined coefficient is larger than the preset determined coefficient threshold;
the acquisition logic for determining the coefficient W is:
comprehensively processing satisfaction Dq of the patient and executable duration Eq of the patient, establishing a data processing model, and generating a determination coefficient W according to the following formula:
wherein, α and γ are respectively preset proportionality coefficients of satisfaction Dq of the patient and executable duration Eq of the patient, and α and γ are both greater than 0, and specific values of the proportionality coefficients α and γ are set by those skilled in the art according to specific situations, and are not limited herein.
Comparing the determined coefficient W with a preset determined coefficient threshold WA, if the determined coefficient W is larger than the preset determined coefficient threshold WA, the current scheme is higher in the aspects of acceptance and executable of the patient, and the current scheme can be used as a final diagnosis and treatment scheme; the doctor can send the regimen to the patient through the intelligent reminder module.
If the determined coefficient W is less than the predetermined determined coefficient threshold WA, it is indicated that the current regimen requires further adjustment in terms of patient satisfaction and length of executable time. The doctor will modify the regimen based on the secondary nutrient regimen, possibly including adjusting the diet plan, changing food selection or adjusting intake, etc. Then, the doctor re-collects the satisfaction degree of the patient and the executable time length of the patient, calculates the determination coefficient again, and carries out loop iteration until the determination coefficient reaches the threshold meeting the requirement.
Claims (5)
1. The clinical nutrition diagnosis and treatment simulation system is characterized by comprising a patient information module, a nutrition evaluation module, a neural network training module, a diagnosis and treatment scheme making module, a doctor reporting module and an intelligent reminding module, wherein the modules are connected through signals;
patient information module: the model training module is used for acquiring and storing basic information of a patient and transmitting the data to the model training module;
nutrition evaluation module: for collecting and analyzing physiological, nutritional and clinical data of a patient and communicating the data to a model training module;
the neural network training module: training a patient by using a neural network based on the data uploaded by the patient information module and the nutrition evaluation module to obtain a preliminary personalized nutrition diagnosis and treatment scheme, and transmitting the obtained data to a diagnosis and treatment scheme making module;
diagnosis and treatment scheme making module: according to the data uploaded by the neural network training module, the obtained primary personalized nutrition diagnosis and treatment scheme is finely modified by combining with the information of the patient, a secondary nutrition diagnosis and treatment scheme is obtained, and the modified secondary nutrition diagnosis and treatment scheme is transmitted to the doctor reporting module;
doctor reporting module: the modified diagnosis and treatment scheme uploaded by the diagnosis and treatment scheme making module is transmitted to a doctor, the doctor modifies or does not modify according to the specific physical condition of the patient, and the final result is transmitted to the intelligent reminding module;
and the intelligent reminding module is used for: and according to the final diagnosis and treatment scheme information uploaded by the doctor reporting module, the intelligent reminding module sends personalized reminding and guiding information to the patient.
2. The clinical nutrition diagnostic modeling system of claim 1, wherein the patient information module includes data of age, sex, past medical history, and body fat rate of the patient.
3. The clinical nutrition diagnosis and treatment simulation system according to claim 1, wherein the nutrition evaluation module comprises average daily nutrition intake of the patient and the up-to-standard rate of the metabolism of the patient;
the acquisition logic of the metabolic standard reaching rate of the patient is as follows: sh= (Sn/Sf-Sg), wherein Sh is the patient metabolism standard rate and Sg is the standard metabolism standard rate; sn is the average daily energy metabolized by the patient and Sf is the average daily energy ingested by the patient.
4. The clinical nutrition diagnostic simulation system of claim 1, wherein said physician reporting module comprises the steps of:
s1: the doctor receives the secondary nutrition diagnosis and treatment scheme uploaded by the diagnosis and treatment scheme making module, and communicates the secondary nutrition diagnosis and treatment scheme with the patient to obtain the satisfaction degree of the patient and the executable time of the patient;
s2: marking satisfaction degree of a patient and executable duration of the patient as Dq and Eq respectively, establishing a determination coefficient W according to the satisfaction degree Dq of the patient and the executable duration Eq of the patient, and comparing the determination coefficient W with a preset determination coefficient threshold WA;
s3: if the determined coefficient W is larger than a preset determined coefficient threshold WA, determining the secondary nutrition diagnosis and treatment scheme as a final diagnosis and treatment scheme of the patient, and sending the final diagnosis and treatment scheme to the patient through an intelligent reminding module;
s4: if the determined coefficient W is smaller than the preset determined coefficient threshold WA, modifying the secondary nutrition diagnosis and treatment scheme by a doctor, and collecting the satisfaction Dq of the patient and the executable duration Eq of the patient again until the determined coefficient W is larger than the preset determined coefficient threshold WA.
5. The clinical nutrition diagnosis and treatment simulation system according to claim 4, wherein the acquiring logic of the determining coefficient W is:
comprehensively processing satisfaction Dq of the patient and executable duration Eq of the patient, establishing a data processing model, and generating a determination coefficient W according to the following formula:
wherein, alpha and gamma are respectively preset proportionality coefficients of satisfaction Dq of the patient and executable duration Eq of the patient, and the alpha and the gamma are both larger than 0.
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| CN202311756714.0A CN117637114A (en) | 2023-12-19 | 2023-12-19 | Clinical nutrition diagnosis and treatment simulation system |
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